Method and system for diagnosing rear axle gear wear
By installing a piezoelectric sensing network on the outside of the rear axle housing, and using ultrasonic guided waves and nonlinear acoustic parameters to assess the degree of wear, combined with a wear fracture condition model, the problem of early warning in existing technologies is solved, and non-intrusive real-time monitoring and early warning of mass-produced vehicles are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XUZHOU HONGRUNDA ELECTRIC VEHICLE CO LTD
- Filing Date
- 2025-11-12
- Publication Date
- 2026-07-14
AI Technical Summary
Existing methods for diagnosing rear axle gear wear rely on periodic disassembly and inspection or internal sensors, which makes it difficult to provide early warnings and is not applicable to mass-produced vehicles.
By installing a piezoelectric sensing network on the outside of the rear axle housing, using piezoelectric ceramic exciters and receivers to transmit and receive ultrasonic guided waves, nonlinear acoustic parameters are calculated, and a wear and fracture condition matching model is used for real-time monitoring and early warning.
It enables real-time monitoring and early warning of the wear condition of the rear axle gears, improving the feasibility and adaptability of monitoring, making it suitable for mass-produced vehicles, and reducing the cost and difficulty of testing.
Smart Images
Figure CN121475671B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mechanical fault diagnosis technology, specifically to a method and system for diagnosing rear axle gear wear. Background Technology
[0002] In modern automotive powertrain systems, rear axle gear wear and breakage are critical factors affecting vehicle performance and safety. Traditional detection methods rely on periodic disassembly and inspection or reactive repair after a failure, making early warning and precise maintenance difficult. Furthermore, with the advancement of vehicle technology, the demand for real-time monitoring and predictive maintenance of rear axle gear wear is increasing. However, existing monitoring technologies mostly rely on internal sensors, the installation of which often requires modifications to the rear axle structure, making them unsuitable for mass-produced vehicles. Summary of the Invention
[0003] This application provides a method and system for diagnosing rear axle gear wear, which addresses the technical problem that existing rear axle gear wear diagnosis methods rely on periodic disassembly and inspection or internal sensors, making it difficult to achieve early warning and unsuitable for mass-produced vehicles.
[0004] The first aspect of this application provides a method for diagnosing rear axle gear wear. The method includes: collecting historical wear and fracture records of gears of the same type as the rear axle gear; analyzing the matching relationship between the wear degree and operating conditions at the time of gear fracture; establishing a wear and fracture operating condition matching model; installing a piezoelectric sensing network outside the rear axle housing corresponding to the rear axle gear, including at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver; transmitting guided waves in the ultrasonic frequency band to the rear axle housing through the piezoelectric ceramic actuator; receiving the ultrasonic guided wave response signal propagating through the rear axle housing using the piezoelectric ceramic receiver; processing the ultrasonic guided wave response signal; calculating nonlinear acoustic parameters, including the ratio of the amplitude of preset higher harmonic components to the amplitude of the fundamental component; evaluating the wear degree of the rear axle gear; performing a matching analysis of the wear degree of the rear axle gear according to the wear and fracture operating condition matching model; and outputting a predicted fracture matching operating condition for alert.
[0005] A second aspect of this application provides a rear axle gear wear diagnosis system, the system comprising: a wear condition matching module for collecting historical wear and fracture records of gears of the same type as the rear axle gear, analyzing the matching relationship between the wear degree and the working condition at the time of gear fracture, and establishing a wear and fracture working condition matching model; a piezoelectric sensor network installation module for installing a piezoelectric sensor network on the exterior of the rear axle housing corresponding to the rear axle gear, including at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver; a response signal receiving module for transmitting guided waves in the ultrasonic frequency band to the rear axle housing through the piezoelectric ceramic actuator, and receiving the ultrasonic guided wave response signal propagated through the rear axle housing using the piezoelectric ceramic receiver; a wear degree assessment module for processing the ultrasonic guided wave response signal, calculating nonlinear acoustic parameters, including the ratio of the amplitude of preset high-order harmonic components to the amplitude of the fundamental component, and assessing the wear degree of the rear axle gear; and a fracture working condition prediction module for performing a matching analysis of the wear degree of the rear axle gear according to the wear and fracture working condition matching model, and outputting a predicted fracture matching working condition for reminder.
[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0007] The rear axle gear wear diagnosis method and system provided in this application relate to the field of mechanical fault diagnosis technology. By installing a piezoelectric sensing network on the outside of the rear axle housing, transmitting and receiving ultrasonic guided waves, calculating nonlinear acoustic parameters to assess the degree of wear, and combining it with a wear fracture condition matching model to predict the risk of gear fracture, the method achieves real-time monitoring and early warning of the wear state of the rear axle gear. This solves the technical problem that existing rear axle gear wear diagnosis methods rely on periodic disassembly and inspection or internal sensors, making it difficult to achieve early warning and unsuitable for mass-produced vehicles. The method achieves non-invasive real-time monitoring and early wear warning by installing a piezoelectric sensing network on the outside of the rear axle housing, thus improving the feasibility and adaptability of monitoring. Attached Figure Description
[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0009] Figure 1 This is a schematic diagram of the rear axle gear wear diagnosis method provided in the embodiments of this application.
[0010] Figure 2 This is a schematic diagram of the rear axle gear wear diagnosis system provided in an embodiment of this application.
[0011] Explanation of reference numerals in the attached diagram: Wear condition matching module 11, piezoelectric sensor network installation module 12, response signal receiving module 13, wear degree assessment module 14, fracture condition prediction module 15. Detailed Implementation
[0012] This application provides a method and system for diagnosing rear axle gear wear, which addresses the technical problem that existing rear axle gear wear diagnosis methods rely on periodic disassembly and inspection or internal sensors, making it difficult to achieve early warning and unsuitable for mass-produced vehicles.
[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.
[0015] Example 1, as Figure 1 As shown, this application provides a method for diagnosing rear axle gear wear, the method comprising:
[0016] P10: Collect historical wear and fracture records of the same type of rear axle gears, analyze the matching relationship between the wear degree and working conditions when the gear fractures, and establish a wear and fracture working condition matching model.
[0017] Furthermore, step P10 in this embodiment of the application also includes:
[0018] P11: Each set of recorded data in the historical wear and fracture records includes image data and operating condition data of the same type of gear at the time of fracture. The operating condition data includes the instantaneous torque, continuous vehicle speed, and safe operating time at the time of fracture. P12: The image data is labeled with wear areas, and the ratio of the wear area to the effective force-bearing area of the gear is calculated as the degree of fracture wear. P13: The degree of fracture wear is correlated with the corresponding operating condition data through regression analysis to establish a wear and fracture operating condition matching model with the degree of fracture wear as input and the instantaneous torque, continuous vehicle speed, and safe operating time at the time of gear fracture as output.
[0019] It should be understood that collecting and analyzing historical wear and fracture records of rear axle gears, and establishing a wear and fracture condition matching model accordingly, is necessary to predict the likelihood of gear fracture.
[0020] First, historical wear and fracture records of the same type of rear axle gears are collected. Each set of records should include image data and operating condition data of the gear at the time of fracture. The image data, obtained by capturing images of the gear at the time of fracture, primarily reflects the specific wear condition of the gear surface at that point. The image data should cover all key parts of the gear, especially potential wear areas, and the images should have sufficient resolution to clearly identify and label the wear areas. The operating condition data should include the specific operating conditions at the time of gear fracture, such as instantaneous torque, continuous vehicle speed, and safe operating time. Instantaneous torque refers to the immediate torque load on the gear at the time of fracture, which directly affects the gear's stress state; continuous vehicle speed is the vehicle's operating speed at the time of gear fracture, which is closely related to the gear's load and wear rate; safe operating time refers to the time the gear can maintain safe operation before fracture, providing further time-dimensional information for operating condition matching.
[0021] Subsequently, the acquired image data is processed, primarily involving the annotation and analysis of the wear areas. This process can be completed using the automatic recognition function of image processing software, combined with manual assistance, to ensure the accuracy of the annotation. For example, the annotation of the image of the gear fracture site can clearly identify the specific location of the damage, providing a basis for subsequent analysis. After annotation, the degree of gear wear can be quantified by calculating the ratio between the area of the wear region and the effective stress area of the gear. This ratio, as an important quantitative indicator of wear degree, can more accurately describe the damage level of the gear and provide data support for the establishment of wear models. A higher ratio of wear area to effective stress area indicates more severe gear damage, potentially leading to a higher risk of fracture.
[0022] Finally, the quantified fracture wear degree was correlated with the corresponding operating condition data through regression analysis. Regression analysis can identify the relationship between wear degree and torque, vehicle speed, and safe operating time using a mathematical model. Based on this relationship, a wear fracture operating condition matching model was established, with wear degree as input and instantaneous torque, continuous vehicle speed, and safe operating time as outputs. This model can predict the probability of fracture under specific operating conditions based on the current wear state of the gear. Through this model, the health status of the gear can be assessed in real time, and potential fracture events can be predicted in a timely manner, providing a scientific basis for gear maintenance and replacement.
[0023] Furthermore, to obtain the safe operating time, step P11 in this embodiment of the application also includes:
[0024] P11-1: Extract the longest period of time during which the vehicle is continuously in a stable operating condition consisting of instantaneous torque and continuous speed before the gear fracture event occurs, and generate the safe operating time; P11-2: If the stable operating condition is entered multiple times before the fracture, the durations of all the times are summed to obtain the safe operating time.
[0025] Optionally, the process of obtaining safe operating time can be further refined to ensure accurate assessment of the gear's operating status and wear under different conditions. First, the longest period of time the vehicle was under stable operating conditions before the gear fracture event is extracted. Stable operating conditions are defined as a period in which the vehicle's instantaneous torque and continuous speed remain relatively constant with minimal fluctuations. In this process, the system extracts torque and speed information from historical operating condition data at different time periods and analyzes this data. For example, by evaluating torque and speed fluctuations, it identifies which periods exhibit smaller fluctuations, meeting the requirements of stable operating conditions. These stable operating condition periods reflect the gear's operation under relatively constant loads and provide a valid reference for subsequent analysis. Among all periods meeting stable operating condition conditions, the longest duration is further selected as the safe operating time. This longest period represents the maximum time the gear can continuously operate under stable loads, providing fundamental data for subsequent wear assessment.
[0026] Next, considering that the vehicle may enter a stable operating condition multiple times in actual operation, further processing is performed on all periods of entering a stable operating condition. If the vehicle enters a stable operating condition multiple times before gear fracture, each period of entry needs to be recorded and analyzed separately, including identifying all periods of entering a stable operating condition and recording the duration of these periods. Finally, by accumulating each period of entering a stable operating condition, a comprehensive safe operating time is obtained. This accumulation process ensures that the safe operating time under different operating conditions is fully considered, thereby more comprehensively assessing the wear state and fracture risk of the gear. This time period not only reveals the gear's operating capability under various stable operating conditions but also provides more comprehensive and accurate input data for subsequent wear and fracture condition matching models, thus helping to improve the accuracy of gear health monitoring and fracture prediction.
[0027] P20: A piezoelectric sensing network is installed on the outside of the rear axle housing corresponding to the rear axle gear, including at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver.
[0028] The piezoelectric ceramic actuator and the piezoelectric ceramic receiver are located in a predetermined area on the rear axle housing, and the center-to-center distance between the piezoelectric ceramic actuator and the piezoelectric ceramic receiver in the predetermined area is 3 to 5 times the wavelength of the selected ultrasonic guided wave.
[0029] Furthermore, the method for determining the preset region is as follows:
[0030] P21: The continuous structure area after the discontinuous structure is identified and eliminated in the rear axle housing is designated as the first screening area; P22: In the first screening area, according to the distance constraint that the center distance between the piezoelectric ceramic exciter and the piezoelectric ceramic receiver is 3 to 5 times the wavelength of the selected ultrasonic guide wave, the position closest to the gear meshing point is selected to determine the preset area.
[0031] Specifically, a piezoelectric sensing network is installed on the exterior of the rear axle housing corresponding to the rear axle gear. This network includes at least one piezoelectric ceramic exciter and one piezoelectric ceramic receiver. Direct vibration and fluid signal sensors, such as accelerometers and online oil quality sensors, need to be installed inside the gearbox or very close to the meshing point, which typically requires modification of the rear axle structure, making it virtually impossible to implement in mass-produced vehicles. Therefore, this application chooses to install the sensors on the exterior of the rear axle housing to avoid modifying the rear axle structure.
[0032] To ensure effective installation and signal transmission of the piezoelectric ceramic actuator and receiver, it is necessary to determine their designated areas on the rear axle housing. The specific steps are as follows: First, a detailed structural analysis of the rear axle housing is performed to identify all discontinuities that may affect the propagation of ultrasonic guided waves, such as welds and bolted connections. The locations of these discontinuities are marked on the rear axle housing for elimination in subsequent steps. Then, a continuous structural area is selected, ensuring that the ultrasonic guided waves do not encounter these discontinuities during propagation, thereby guaranteeing signal integrity and accuracy. This area will serve as the first screening area for the subsequent installation of the piezoelectric ceramic actuator and receiver.
[0033] Next, within the first screening area, the corresponding wavelength is calculated based on the selected ultrasonic guided wave frequency. The position layout is planned according to the requirement that the center-to-center distance between the piezoelectric ceramic exciter and the piezoelectric ceramic receiver be 3 to 5 times the wavelength. Based on this, the position closest to the gear meshing point is selected as the preset area to ensure that the sensor can receive signals from the gear meshing area to the maximum extent, thereby improving the sensitivity and accuracy of monitoring.
[0034] P30: The piezoelectric ceramic exciter emits ultrasonic guided waves into the rear axle housing, and the piezoelectric ceramic receiver receives the ultrasonic guided wave response signal after propagation through the rear axle housing.
[0035] Optionally, piezoelectric ceramic actuators and receivers can be used to transmit and receive ultrasonic guided waves to monitor the wear of the rear axle gears. Specifically, after installing the piezoelectric sensing network and determining the preset area, the piezoelectric ceramic actuator is activated to transmit ultrasonic guided waves into the rear axle housing. Ultrasonic guided waves are a technology that utilizes high-frequency sound wave propagation; they can propagate effectively in solid media and have strong penetrating power. The piezoelectric ceramic actuator, as a signal source, primarily converts electrical signals into mechanical vibrations and transmits ultrasonic guided waves through the rear axle housing. The transmitted ultrasonic guided waves propagate along the rear axle housing, passing through the gears and surrounding structures, forming signals sensitive to the condition of the gears and rear axle. These ultrasonic guided waves can interact with defects, damage, or wear areas in the gears and rear axle housing during propagation; phenomena such as reflection and refraction cause changes in the signal, thus carrying important information about the gear condition.
[0036] As ultrasonic guided waves propagate through the rear axle housing, they encounter various structural features, including the meshing areas of gears. If the gears are worn, these worn areas will scatter and reflect the ultrasonic guided waves, thus altering their propagation characteristics. These changes are captured by a piezoelectric ceramic receiver mounted externally to the housing. After receiving the ultrasonic guided wave response signal propagating through the rear axle housing, the piezoelectric ceramic receiver can extract characteristic parameters related to gear wear by analyzing the characteristics of these signals, such as amplitude, phase, and frequency distribution.
[0037] To ensure signal quality and reliability, a series of parameter optimizations and calibrations are required during transmission and reception. For example, the transmission frequency of the piezoelectric ceramic actuator needs to be adjusted to match the propagation characteristics of the rear axle housing to achieve optimal signal transmission. Simultaneously, the sensitivity of the piezoelectric ceramic receiver also needs to be calibrated to ensure accurate detection of even minute signal variations.
[0038] During this process, the propagation characteristics of ultrasonic guided waves allow the signal to penetrate the entire rear axle housing and reflect the interaction between the gear and other structures. Especially when the gear is worn or has defects, the propagation characteristics of the ultrasonic guided waves change significantly. By accurately detecting and analyzing these changes, the degree of gear wear and potential damage areas can be effectively assessed, thus providing a basis for subsequent health monitoring, early warning, and maintenance decisions.
[0039] P40: Process the ultrasonic guided wave response signal, calculate nonlinear acoustic parameters, including the ratio of the amplitude of preset higher harmonic components to the amplitude of the fundamental component, and evaluate the wear degree of the rear axle gear. The preset higher harmonic components include at least the second and third harmonics.
[0040] Furthermore, step P40 in this embodiment of the application also includes:
[0041] P41: After filtering the ultrasonic guided wave response signal, perform a fast Fourier transform to obtain the guided wave response spectrum; P42: In the guided wave response spectrum, identify the amplitude of the fundamental component and the amplitude of the preset higher harmonic components, and calculate the nonlinear acoustic parameters; P43: Match the nonlinear acoustic parameters with a pre-calibrated nonlinear parameter-wear degree lookup table, and output the wear degree of the rear axle gear.
[0042] It should be understood that processing the ultrasonic guided wave response signal, calculating nonlinear acoustic parameters, and using these parameters to assess the wear degree of the rear axle gear are crucial. Nonlinear acoustic characteristics can reflect the effects of physical changes such as gear wear; therefore, by calculating and analyzing these parameters, the health status of the gear can be accurately determined. Specifically, the ultrasonic guided wave response signal first needs to be processed to calculate the nonlinear acoustic parameters, which is the ratio of the amplitude of higher harmonic components to the amplitude of the fundamental component. Higher harmonic components typically include the second and third harmonics, which are signal components generated by the nonlinear effects during the propagation of the ultrasonic guided wave. The ratio of the amplitude of these higher harmonics to the amplitude of the fundamental wave is a key indicator for assessing the degree of gear wear. As gear wear increases, the nonlinear effect of the ultrasonic guided wave intensifies, leading to an increase in the amplitude of the higher harmonic components. Therefore, by calculating the ratio of these harmonic components, the degree of gear wear can be reflected.
[0043] For example, the acquired ultrasonic guided wave response signal is first filtered to remove noise and other interference signals. Filtering can employ digital filtering techniques, such as low-pass filtering, high-pass filtering, or band-pass filtering, to ensure signal purity. The filtered signal will be clearer, aiding in subsequent analysis and processing.
[0044] Next, a Fast Fourier Transform (FFT) is performed on the filtered ultrasonic guided wave response signal to obtain the guided wave response spectrum. The FFT converts the time-domain signal into a frequency-domain signal, thus obtaining the spectrum of the guided wave response. The spectrum clearly shows the amplitude of each frequency component in the signal, including the fundamental frequency and higher harmonics. The Fast Fourier Transform efficiently extracts characteristic information in the frequency domain and is an important tool for analyzing ultrasonic signals.
[0045] In the guided wave response spectrum diagram, the amplitudes of the fundamental frequency component and the preset amplitudes of higher harmonic components are identified. The fundamental frequency component represents the basic frequency of the ultrasonic guided wave, while the higher harmonic components are signal components caused by the nonlinear effects of the system. After identifying these spectral components, the nonlinear acoustic parameter, namely the ratio of the higher harmonic amplitude to the fundamental frequency amplitude, is calculated. This ratio is a key indicator reflecting the degree of gear wear and can reveal the changes in the gear during operation through nonlinear acoustic effects.
[0046] Finally, the calculated nonlinear acoustic parameters are matched with a pre-calibrated nonlinear parameter-wear degree comparison table. This pre-calibrated table, established through experiments and data analysis, contains typical values of the nonlinear acoustic parameters under different wear degrees. By matching with this table, the current wear degree of the rear axle gear can be accurately determined, providing an important basis for subsequent early warning and maintenance decisions.
[0047] Furthermore, in the construction of the nonlinear parameter-wear degree comparison table, step P43 of this application embodiment also includes:
[0048] P43-1: Accelerated life test using identical gear pairs, with the test paused at different wear stages; P43-2: At each pause stage, the wear of the gears is measured, and the standard nonlinear acoustic parameters for each pause stage are identified and calculated using a piezoelectric ceramic exciter and a piezoelectric ceramic receiver, establishing multiple sets of wear degree-nonlinear parameter mapping pairs; P43-3: Based on the multiple sets of wear degree-nonlinear parameter mapping pairs, the nonlinear parameter-wear degree comparison table is generated through curve fitting.
[0049] Optionally, the construction process of the nonlinear parameter-wear degree comparison table can be further refined to ensure the accuracy and reliability of the evaluation results. First, accelerated life testing is conducted using identical gear pairs. Accelerated life testing is a method that accelerates the gear wear process by applying stresses (such as loads and speeds) higher than normal operating conditions. During the test, the gear pair will experience different wear stages. At each wear stage, the system pauses the test to allow for detailed wear measurements and data recording. The purpose of pausing the test is to ensure that wear data at each stage can be accurately collected, ensuring the accuracy of the comparison table.
[0050] Next, at each pause stage, the wear of the gears was measured. Wear can be measured using various methods, such as measuring gear dimensional changes with high-precision measuring tools or observing wear marks on the gear surface under a microscope. Simultaneously, at each pause stage, ultrasonic guided wave signals were transmitted and received using an installed piezoelectric ceramic actuator and receiver, and standard nonlinear acoustic parameters for each pause stage were calculated. Through multiple pause tests and data acquisition, multiple sets of mapping data between different wear levels and nonlinear acoustic parameters were obtained. These data reflect the relationship between wear levels and nonlinear acoustic parameters, providing a foundation for the subsequent establishment of a comparison table.
[0051] Finally, based on the obtained mapping data of multiple sets of wear degree and nonlinear parameters, data processing was performed through curve fitting to generate a nonlinear parameter-wear degree comparison table. Curve fitting is a mathematical method used to generate a function that describes the data relationship based on known data points. In this process, an appropriate fitting model can be selected, such as linear fitting, polynomial fitting, or exponential fitting, to ensure that the comparison table accurately reflects the relationship between wear degree and nonlinear parameters. The generated comparison table can accurately reflect the acoustic characteristics of gears at different wear stages and provide a reliable basis for subsequent wear degree assessment.
[0052] P50: Based on the wear and fracture condition matching model, the wear degree of the rear axle gear is matched and analyzed, and the predicted fracture matching condition is output as a reminder.
[0053] Furthermore, step P50 in this embodiment of the application also includes:
[0054] P51: Real-time acquisition of the current torque and current vehicle speed of the vehicle corresponding to the rear axle gear, and matching in the predicted fracture matching condition. If the matching is successful, output the predicted safe operating time that triggers the gear fracture event; P52: Send the predicted safe operating time to the target user to remind them of abnormal gear wear.
[0055] It should be understood that the wear degree of the rear axle gear is matched and analyzed using the wear fracture condition matching model, and the predicted fracture matching condition is output through this analysis, thereby issuing an early warning.
[0056] First, the vehicle's operating condition information is acquired in real time and compared with the established predicted fracture matching conditions. Specifically, the current torque and vehicle speed data corresponding to the rear axle gear are acquired in real time. This data reflects the vehicle's current workload and driving state, and is a key factor in determining whether gear fracture is likely. Next, this data is matched with the predicted fracture conditions established in the wear fracture condition matching model. If the match is successful, it means that the current vehicle operating condition matches the predicted condition that may lead to gear fracture, and the predicted safe operating time under this condition can be output. This predicted safe operating time is calculated based on the vehicle's current workload and operating conditions, estimating the time the gear can still operate safely under the current conditions.
[0057] Finally, the predicted safe operating time is sent to the target users as a reminder of abnormal gear wear. Target users can be vehicle drivers, maintenance personnel, or operators of the vehicle management system. The reminder can be sent in various ways, such as through the vehicle's dashboard display, mobile application push notifications, or SMS messages. The reminder message should include specific values for the predicted safe operating time, as well as recommended maintenance or inspection measures, so that users can take timely action to prevent gear breakage.
[0058] Furthermore, after sending the predicted safe operating time to the target user to alert them to abnormal gear wear, step P50 in this embodiment of the application also includes:
[0059] P53: If a change is detected in the current torque and current speed of the vehicle corresponding to the rear axle gear, the changed torque and speed are used to match the predicted fracture matching condition to obtain the changed predicted safe operating time; P54: Calculate the difference between the changed predicted safe operating time and the initial output predicted safe operating time, and issue a gear fracture probability guidance prompt signal under the change of operating condition.
[0060] Specifically, the monitoring and alerting mechanism for rear axle gear wear can be further expanded, particularly in dynamic response to changes in vehicle operating conditions. After sending the predicted safe operating time to the target user to alert them of abnormal gear wear, the system continues to monitor the vehicle's operating status. If a change is detected in the current torque and vehicle speed corresponding to the rear axle gear, it means that the load and operating conditions borne by the rear axle gear have also changed, which may affect the wear process and breakage risk of the gear. Therefore, the system will immediately acquire the changed torque and speed data and use this updated operating condition data to re-match with the predicted breakage conditions in the wear-breakage matching model. Through this matching process, a new predicted safe operating time can be calculated based on the changed vehicle operating conditions, that is, the time during which the gear may continue to operate safely under the new conditions.
[0061] Subsequently, the revised predicted safe operating time is compared with the initial predicted safe operating time, and the difference between the two is calculated. Due to changes in vehicle torque and speed, the new predicted safe operating time may differ from the initial prediction. This difference reflects the impact of the changing operating conditions on gear wear and breakage risk. By calculating this difference, the system can assess whether the probability of gear breakage has increased under the current changing operating conditions. Based on this difference, a gear breakage probability guidance signal under the changed operating conditions can be generated. This signal clearly indicates to the target user how the gear breakage probability has changed due to the change in operating conditions. For example, if the new predicted safe operating time is significantly shortened, the signal should emphasize the increased risk of gear breakage and advise the user to take immediate action.
[0062] With this extended function, the system can not only monitor changes in vehicle operating conditions in real time, but also dynamically adjust the prediction of safe operating time of gears, thereby providing users with more accurate and timely warnings and further improving the safety and reliability of the vehicle.
[0063] In summary, the embodiments of this application have at least the following technical effects:
[0064] This application utilizes a non-invasive piezoelectric sensor network to monitor the wear state of rear axle gears in real time and issue an early warning when the wear level approaches the fracture threshold, effectively preventing gear fracture accidents. By installing sensors externally on the rear axle housing, no modifications to the vehicle structure are required, reducing detection costs and implementation difficulty, making it suitable for mass-produced vehicles. Utilizing ultrasonic guided waves and nonlinear acoustic parameters, the degree of gear wear can be quantitatively assessed with high precision, providing more accurate monitoring data. During vehicle operation, the current torque and vehicle speed are acquired in real time, and the predicted safe operating time is dynamically adjusted according to changes in operating conditions, ensuring the accuracy and timeliness of monitoring results. Based on a wear-fracture condition matching model, the risk of gear fracture is predicted, providing a scientific basis for predictive maintenance, reducing unexpected downtime and repair costs, and significantly improving vehicle operating safety and maintenance efficiency.
[0065] The technology achieves the goal of non-invasive real-time monitoring and early wear warning by installing a piezoelectric sensing network on the outside of the rear axle housing, thereby improving the feasibility and adaptability of monitoring.
[0066] Example 2, based on the same inventive concept as the rear axle gear wear diagnosis method in the aforementioned examples, such as... Figure 2 As shown, this application provides a rear axle gear wear diagnosis system. The system and method embodiments in this application are based on the same inventive concept. The system includes:
[0067] Wear condition matching module 11 is used to collect historical wear and fracture records of the same type of gears of the rear axle gear, analyze the matching relationship between the wear degree and the working condition when the gear fractures, and establish a wear and fracture working condition matching model.
[0068] The piezoelectric sensing network mounting module 12 is used to mount a piezoelectric sensing network on the outside of the rear axle housing corresponding to the rear axle gear, and includes at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver.
[0069] The response signal receiving module 13 is used to transmit guided waves in the ultrasonic frequency band to the rear axle housing through the piezoelectric ceramic exciter, and to receive the ultrasonic guided wave response signal after propagation through the rear axle housing using the piezoelectric ceramic receiver.
[0070] The wear assessment module 14 is used to process the ultrasonic guided wave response signal, calculate nonlinear acoustic parameters, including the ratio of the amplitude of preset higher harmonic components to the amplitude of the fundamental component, and assess the wear degree of the rear axle gear. The preset higher harmonic components include at least the second and third harmonics.
[0071] The fracture condition prediction module 15 is used to perform a matching analysis on the wear degree of the rear axle gear according to the wear fracture condition matching model, and output the predicted fracture matching condition for reminder.
[0072] Furthermore, the wear condition matching module 11 is also used to perform the following steps:
[0073] Each set of recorded data in the historical wear and fracture records includes image data and operating condition data of the same type of gear at the time of fracture. The operating condition data includes the instantaneous torque, continuous vehicle speed, and safe operating time at the time of fracture. The wear area is marked on the image data, and the ratio of the wear area to the effective force-bearing area of the gear is calculated as the degree of fracture wear. The degree of fracture wear is correlated with the corresponding operating condition data through regression analysis to establish a wear and fracture operating condition matching model with the degree of fracture wear as input and the instantaneous torque, continuous vehicle speed, and safe operating time at the time of gear fracture as output.
[0074] Furthermore, the wear condition matching module 11 is also used to perform the following steps:
[0075] From each set of recorded data, the longest period of time during which the vehicle was continuously in a stable operating condition consisting of instantaneous torque and continuous speed before the gear fracture event was extracted, and the safe operating time was generated; if the stable operating condition was entered multiple times before the fracture, all durations were summed to obtain the safe operating time.
[0076] Furthermore, in the piezoelectric sensing network mounting module 12:
[0077] The piezoelectric ceramic actuator and the piezoelectric ceramic receiver are located in a predetermined area on the rear axle housing. The center-to-center distance between the piezoelectric ceramic actuator and the piezoelectric ceramic receiver in the predetermined area is 3 to 5 times the wavelength of the selected ultrasonic guided wave. The predetermined area is determined as follows: the continuous structure area after identifying and eliminating discontinuous structures in the rear axle housing is used as the first screening area; in the first screening area, according to the distance constraint that the center-to-center distance between the piezoelectric ceramic actuator and the piezoelectric ceramic receiver is 3 to 5 times the wavelength of the selected ultrasonic guided wave, the position closest to the gear meshing point is selected to determine the predetermined area.
[0078] Furthermore, the wear assessment module 14 is also used to perform the following steps:
[0079] After filtering the ultrasonic guided wave response signal, a fast Fourier transform is performed to obtain the guided wave response spectrum. In the guided wave response spectrum, the amplitude of the fundamental component and the amplitude of the preset higher harmonic components are identified, and the nonlinear acoustic parameters are calculated. The nonlinear acoustic parameters are matched with a pre-calibrated nonlinear parameter-wear degree lookup table to output the wear degree of the rear axle gear.
[0080] Furthermore, the wear assessment module 14 is also used to perform the following steps:
[0081] Accelerated life tests were conducted using identical gear pairs, with the tests paused at different wear stages. At each pause, the wear of the gears was measured, and piezoelectric ceramic exciters and receivers were used to identify and calculate the standard nonlinear acoustic parameters for each pause, establishing multiple sets of wear degree-nonlinear parameter mapping pairs. Based on these multiple sets of wear degree-nonlinear parameter mapping pairs, a nonlinear parameter-wear degree comparison table was generated through curve fitting.
[0082] Furthermore, the fracture condition prediction module 15 is also used to perform the following steps:
[0083] The system acquires the current torque and speed of the vehicle corresponding to the rear axle gear in real time, performs matching under the predicted fracture matching condition, and outputs the predicted safe operating time that triggers the gear fracture event if the matching is successful. The predicted safe operating time is then sent to the target user to remind them of abnormal gear wear.
[0084] Furthermore, the fracture condition prediction module 15 is also used to perform the following steps:
[0085] If a change is detected in the current torque and current speed of the vehicle corresponding to the rear axle gear, the changed torque and speed are used to match the predicted fracture matching condition to obtain the changed predicted safe operating time; the difference between the changed predicted safe operating time and the initial output predicted safe operating time is calculated, and a gear fracture probability guidance prompt signal is issued under the changed operating condition.
[0086] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0087] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0088] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A method for diagnosing rear axle gear wear, characterized in that, include: Historical wear and fracture records of the same type of rear axle gears were collected. The matching relationship between the wear degree at the time of gear fracture and the operating conditions was analyzed to establish a wear and fracture operating condition matching model. Specifically, each set of data in the historical wear and fracture records includes image data and operating condition data of the same type of gear at the time of fracture. The operating condition data includes the instantaneous torque, continuous vehicle speed, and safe operating time at the time of fracture. The wear area of the image data is marked, and the ratio of the wear area to the effective force-bearing area of the gear is calculated as the degree of fracture wear. The degree of fracture wear and the corresponding operating condition data are correlated and regressed to establish the wear and fracture operating condition matching model with the degree of fracture wear as input and the instantaneous torque, continuous vehicle speed, and safe operating time at the time of gear fracture as output. A piezoelectric sensing network is installed on the outside of the rear axle housing corresponding to the rear axle gear, including at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver. The piezoelectric ceramic exciter emits guided waves in the ultrasonic frequency band into the rear axle housing, and the piezoelectric ceramic receiver receives the ultrasonic guided wave response signal after propagation through the rear axle housing. The ultrasonic guided wave response signal is processed to calculate nonlinear acoustic parameters, including the ratio of the amplitude of a preset higher harmonic component to the amplitude of the fundamental component, to assess the wear degree of the rear axle gear. Specifically, this includes: filtering the ultrasonic guided wave response signal and then performing a fast Fourier transform to obtain a guided wave response spectrum; identifying the amplitude of the fundamental component and the amplitude of the preset higher harmonic components in the guided wave response spectrum, and calculating the nonlinear acoustic parameters; matching the nonlinear acoustic parameters with a pre-calibrated nonlinear parameter-wear degree lookup table, and outputting the wear degree of the rear axle gear. The wear degree of the rear axle gear is matched and analyzed according to the wear and fracture condition matching model, and the predicted fracture matching condition is output for reminder. Specifically, this includes: real-time acquisition of the current torque and current vehicle speed of the vehicle corresponding to the rear axle gear, matching in the predicted fracture matching condition, and if the matching is successful, outputting the predicted safe operating time that triggers the gear fracture event; and sending the predicted safe operating time to the target user to remind them of abnormal gear wear.
2. The rear axle gear wear diagnosis method as described in claim 1, characterized in that, From each set of recorded data, the longest period of time during which the vehicle was continuously in a stable operating condition consisting of instantaneous torque and continuous speed before the gear breakage event was extracted, and the safe operating time was generated. If the stable operating condition is entered multiple times before the fracture, the durations of all occurrences are summed to obtain the safe operating time.
3. The rear axle gear wear diagnosis method as described in claim 1, characterized in that, The steps for constructing the nonlinear parameter-wear degree comparison table include: Accelerated life tests were conducted using identical gear pairs, and the tests were paused at different wear stages. During each pause phase, the wear of the gear is measured, and the standard nonlinear acoustic parameters for each pause phase are identified and calculated using a piezoelectric ceramic exciter and a piezoelectric ceramic receiver, establishing multiple sets of wear degree-nonlinear parameter mapping pairs. Based on the multiple sets of wear degree-nonlinear parameter mapping pairs, the nonlinear parameter-wear degree comparison table is generated by curve fitting.
4. The rear axle gear wear diagnosis method as described in claim 1, characterized in that, The preset higher harmonic components include at least the second harmonic and the third harmonic.
5. The rear axle gear wear diagnosis method as described in claim 1, characterized in that, After sending the predicted safe operating time to the target user as a reminder of abnormal gear wear, the process also includes: If a change is detected in the current torque and current speed of the vehicle corresponding to the rear axle gear, the changed torque and speed are used to match the predicted fracture matching condition to obtain the changed predicted safe operating time. The difference between the predicted safe operating time after the change and the predicted safe operating time of the initial output is calculated, and a gear fracture probability guidance signal is generated under the change of operating conditions.
6. The rear axle gear wear diagnosis method as described in claim 1, characterized in that, The piezoelectric ceramic actuator and the piezoelectric ceramic receiver are located in a predetermined area on the rear axle housing, and the center-to-center distance between the piezoelectric ceramic actuator and the piezoelectric ceramic receiver in the predetermined area is 3 to 5 times the wavelength of the selected ultrasonic guided wave; The method for determining the preset region is as follows: The continuous structure region after the discontinuous structure is identified and eliminated in the rear axle housing is used as the first screening region; In the first screening area, the preset area is determined by selecting the position closest to the gear meshing point, based on the distance constraint that the center distance between the piezoelectric ceramic exciter and the piezoelectric ceramic receiver is 3 to 5 times the wavelength of the selected ultrasonic guided wave.
7. A rear axle gear wear diagnosis system, characterized in that, The system includes: The wear condition matching module is used to collect historical wear and fracture records of the same type of gears in the rear axle, analyze the matching relationship between the wear degree and the working condition at the time of gear fracture, and establish a wear and fracture working condition matching model. Specifically, it includes: each set of recorded data in the historical wear and fracture records includes image data and working condition data of the same type of gear at the time of fracture, and the working condition data includes the instantaneous torque, continuous vehicle speed, and safe operating time at the time of fracture; the wear area is marked on the image data, and the ratio of the wear area to the effective force-bearing area of the gear is calculated as the degree of fracture wear; the degree of fracture wear is correlated with the corresponding working condition data through regression analysis to establish the wear and fracture working condition matching model with the degree of fracture wear as input and the instantaneous torque, continuous vehicle speed, and safe operating time at the time of gear fracture as output. A piezoelectric sensing network mounting module is used to mount a piezoelectric sensing network on the outside of the rear axle housing corresponding to the rear axle gear, and includes at least one piezoelectric ceramic actuator and one piezoelectric ceramic receiver. The response signal receiving module is used to transmit guided waves in the ultrasonic frequency band to the rear axle housing through the piezoelectric ceramic exciter, and to receive the ultrasonic guided wave response signal after propagation through the rear axle housing using the piezoelectric ceramic receiver. The wear assessment module is used to process the ultrasonic guided wave response signal, calculate nonlinear acoustic parameters, including the ratio of the amplitude of preset higher harmonic components to the amplitude of the fundamental component, and assess the wear degree of the rear axle gear. Specifically, it includes: filtering the ultrasonic guided wave response signal and then performing a fast Fourier transform to obtain a guided wave response spectrum; identifying the amplitude of the fundamental component and the amplitude of preset higher harmonic components in the guided wave response spectrum, calculating the nonlinear acoustic parameters; matching the nonlinear acoustic parameters with a pre-calibrated nonlinear parameter-wear degree lookup table, and outputting the wear degree of the rear axle gear. The fracture condition prediction module is used to perform matching analysis on the wear degree of the rear axle gear according to the wear fracture condition matching model, and output the predicted fracture matching condition for reminder. Specifically, it includes: real-time acquisition of the current torque and current vehicle speed of the vehicle corresponding to the rear axle gear, matching in the predicted fracture matching condition, and if the matching is successful, outputting the predicted safe operating time that triggers the gear fracture event; and sending the predicted safe operating time to the target user to remind them of abnormal gear wear.